Project: Apply Lightweight Fine-Tuning to a Foundation Model¶

Introduction¶

Lightweight fine-tuning is one of the most important techniques for adapting foundation models because it allows you to modify foundation models for your needs without needing substantial computational resources. In this project, we will apply parameter-efficient fine-tuning (PEFT) using the Hugging Face PEFT library.

Project Overview¶

  • Load a pre-trained model and evaluate its performance.
  • Perform parameter-efficient fine-tuning using the pre-trained model.
  • Perform inference using the fine-tuned model and compare its performance to the original model.

TODO: In this cell, describe your choices for each of the following

  • PEFT technique: We will use LoRA (Low-Rank Adaptation) as our PEFT technique. LoRA is a method that adapts the weights of a pre-trained model using low-rank matrices, making it computationally efficient and requiring fewer parameters to be fine-tuned. This technique helps in reducing the memory footprint and training time.

  • Model: We will use the ALBERT (A Lite BERT) model from Hugging Face's library. ALBERT is a lightweight version of BERT, designed to have fewer parameters while retaining the performance. This makes it an excellent choice for tasks that require efficiency without compromising on accuracy.

  • Evaluation approach: We will evaluate the model's performance using a sequence classification task. The evaluation will be based on metrics such as accuracy, precision, recall, and F1-score. These metrics will help us understand how well the model is performing in classifying sequences correctly.

  • Fine-tuning dataset: For fine-tuning, we will use the GLUE (General Language Understanding Evaluation) benchmark, specifically the SST-2 (Stanford Sentiment Treebank) dataset. SST-2 is a popular dataset for sentiment analysis, consisting of movie reviews labeled as positive or negative. This dataset is well-suited for evaluating sequence classification tasks and will help us benchmark our fine-tuning process effectively.

Loading a Pre-trained Model and Evaluating its Performance¶

In this section, we will load a pre-trained Hugging Face model and evaluate its performance on a sequence classification task.

In [1]:
!pip install scikit-learn
Defaulting to user installation because normal site-packages is not writeable
Collecting scikit-learn
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Installing collected packages: threadpoolctl, joblib, scikit-learn
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In [2]:
pip install peft
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Note: you may need to restart the kernel to use updated packages.
In [3]:
# Import necessary libraries
from transformers import AlbertTokenizerFast, AlbertForSequenceClassification
from datasets import load_dataset
from transformers import Trainer, TrainingArguments, DataCollatorWithPadding
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import matplotlib.pyplot as plt

Step 1: Load the Pre-trained Model and Tokenizer¶

In [4]:
# Load a pre-trained model and tokenizer
model_name = 'albert-base-v2'
tokenizer = AlbertTokenizerFast.from_pretrained(model_name)
model = AlbertForSequenceClassification.from_pretrained(model_name, num_labels=2)
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Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
In [5]:
# Import necessary libraries
from transformers import AlbertTokenizerFast, AlbertForSequenceClassification
from datasets import load_dataset
from transformers import Trainer, TrainingArguments, DataCollatorWithPadding
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import matplotlib.pyplot as plt

# Step 1: Load the Pre-trained Model and Tokenizer
model_name = 'albert-base-v2'
tokenizer = AlbertTokenizerFast.from_pretrained(model_name)
model = AlbertForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
# You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

# Define the compute_metrics function
def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return {"accuracy": (predictions == labels).mean()}

# Load the dataset
dataset = load_dataset('glue', 'mrpc')
# Tokenize the dataset
def tokenize_function(examples):
    return tokenizer(examples['sentence1'], examples['sentence2'], truncation=True)
tokenized_ds = dataset.map(tokenize_function, batched=True)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=TrainingArguments(
        output_dir=".",
        learning_rate=2e-3,
        per_device_train_batch_size=1,
        per_device_eval_batch_size=1,
        num_train_epochs=0,
        weight_decay=0.01,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
    ),
    train_dataset=tokenized_ds["train"],
    eval_dataset=tokenized_ds["test"],
    tokenizer=tokenizer,
    data_collator=DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length"),
    compute_metrics=compute_metrics,
)

# Evaluate the pretrained model
pretrained_model_eval_results = trainer.evaluate()
print("Pretrained Model Evaluation Results:", pretrained_model_eval_results)
Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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You're using a AlbertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
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Pretrained Model Evaluation Results: {'eval_loss': 0.664569616317749, 'eval_accuracy': 0.6243478260869565, 'eval_runtime': 66.0755, 'eval_samples_per_second': 26.107, 'eval_steps_per_second': 26.107}

Step 2: Load and Preprocess the Dataset¶

In [6]:
# Load and preprocess the dataset
raw_datasets = load_dataset('imdb')

def tokenize_function(example):
    return tokenizer(example['text'], padding='max_length', truncation=True)

tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)

# Split the dataset
train_testvalid = tokenized_datasets['train'].train_test_split(test_size=0.1)
tokenized_datasets = train_testvalid['train'].train_test_split(test_size=0.1)
tokenized_datasets['validation'] = train_testvalid['test']

# Debugging: Check the columns of the dataset
print('Columns in the dataset:', tokenized_datasets['train'].column_names)

# Debugging: Check the size of each split
print('Size of train dataset:', len(tokenized_datasets['train']))
print('Size of validation dataset:', len(tokenized_datasets['validation']))
print('Sample data from train dataset:', tokenized_datasets['train'][0])
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Columns in the dataset: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask']
Size of train dataset: 20250
Size of validation dataset: 2500
Sample data from train dataset: {'text': "This film's kind of like Conan the Barabarian, but with more sex, rape and murder. There is a plot somewhere underneath all this debauchery but the filmmakers don't do a good job showing it, which is a shame because it 'could' be a decent story. Richard Hill gives a solid performance in the lead role, as does the villain - who sadly didn't appear in anything else of note. The fight scenes aren't too bad either - I love the way Deathstalker lets his sword 'drink' the blood of his victims - and there's plenty of nudity and sex to temper the general level of machismo throughout. <br /><br />All in all, not good - but not necessarily that bad either...", 'label': 0, 'input_ids': [2, 48, 171, 22, 18, 825, 16, 101, 18657, 14, 748, 2297, 7142, 15, 47, 29, 91, 1589, 15, 8253, 17, 2244, 9, 80, 25, 21, 3798, 3493, 7170, 65, 48, 121, 17475, 3462, 93, 47, 14, 19485, 221, 22, 38, 107, 21, 254, 1205, 3187, 32, 15, 56, 25, 21, 7912, 185, 32, 13, 22, 13431, 22, 44, 21, 12238, 609, 9, 1098, 948, 2352, 21, 2941, 956, 19, 14, 672, 597, 15, 28, 630, 14, 12353, 13, 8, 72, 16382, 223, 22, 38, 1893, 19, 602, 962, 16, 1945, 9, 14, 1074, 3918, 4847, 22, 38, 266, 896, 694, 13, 8, 31, 339, 14, 161, 7251, 9718, 106, 6884, 33, 2960, 13, 22, 43, 10603, 22, 14, 696, 16, 33, 4397, 13, 8, 17, 80, 22, 18, 7062, 16, 29067, 17, 1589, 20, 11617, 14, 297, 662, 16, 10604, 21776, 892, 9, 13, 1, 5145, 13, 118, 1, 5145, 13, 118, 1, 1233, 19, 65, 15, 52, 254, 13, 8, 47, 52, 9324, 30, 896, 694, 9, 9, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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In [7]:
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset
import torch

# Load the dataset and tokenizer
raw_datasets = load_dataset('imdb')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

def tokenize_function(example):
    return tokenizer(example['text'], padding='max_length', truncation=True)

tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)

# Split the dataset
train_testvalid = tokenized_datasets['train'].train_test_split(test_size=0.1)
tokenized_datasets = train_testvalid['train'].train_test_split(test_size=0.1)
tokenized_datasets['validation'] = train_testvalid['test']

# Debugging: Check the columns of the dataset
print('Columns in the dataset:', tokenized_datasets['train'].column_names)

# Check the first few examples in the tokenized dataset
print('First few examples in the tokenized dataset:')
for i, example in enumerate(tokenized_datasets['train']):
    if i < 3:
        print(f'Example {i}: {example}')

# Load the model
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')

# Inspect the model architecture
print(model)

from peft import get_peft_model, LoraConfig

# Initialize the PEFT configuration with updated target modules
peft_config = LoraConfig(
    target_modules=["bert.encoder.layer.0.attention.self.query", "bert.encoder.layer.0.attention.self.key"],  # Update this based on the model inspection
    r=8,
    lora_alpha=32,
    lora_dropout=0.1,
    bias="none",
)

# Apply PEFT to the model
peft_model = get_peft_model(model, peft_config)

# Training configuration
training_args = TrainingArguments(
    output_dir='./results',          # output directory
    num_train_epochs=3,              # total number of training epochs
    per_device_train_batch_size=8,   # batch size for training
    per_device_eval_batch_size=8,    # batch size for evaluation
    warmup_steps=500,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    logging_dir='./logs',            # directory for storing logs
    logging_steps=10,
)

# Ensure inputs are correctly passed during training
def data_collator(features):
    # Debugging: print out the keys and values of each feature in the batch
    for i, feature in enumerate(features):
        print(f"Feature {i} keys: {feature.keys()}")
        for key in feature.keys():
            print(f"Feature {i} - {key}: {feature[key]}")

    # Ensure each feature has 'input_ids', 'attention_mask', and 'token_type_ids'
    for feature in features:
        if 'input_ids' not in feature:
            feature['input_ids'] = torch.zeros(tokenizer.model_max_length, dtype=torch.long)
        if 'attention_mask' not in feature:
            feature['attention_mask'] = torch.zeros(tokenizer.model_max_length, dtype=torch.long)
        if 'token_type_ids' not in feature:
            feature['token_type_ids'] = torch.zeros(tokenizer.model_max_length, dtype=torch.long)

    # Stack tensors and return dictionary
    return {
        'input_ids': torch.stack([f['input_ids'] for f in features]),
        'attention_mask': torch.stack([f['attention_mask'] for f in features]),
        'token_type_ids': torch.stack([f['token_type_ids'] for f in features]),
        'labels': torch.tensor([f['label'] for f in features if 'label' in f]),  # Corrected key to 'label'
    }

# Now train the PEFT model
trainer = Trainer(
    model=peft_model,  # the instantiated PEFT model
    args=training_args,  # training arguments
    train_dataset=tokenized_datasets['train'],  # training dataset
    eval_dataset=tokenized_datasets['validation'],  # evaluation dataset
    data_collator=data_collator,
)

# Additional debugging before training
print("Training configuration:", training_args)
print("Training dataset size:", len(tokenized_datasets['train']))
print("Validation dataset size:", len(tokenized_datasets['validation']))

# Train the PEFT model
trainer.train()

# Save the fine-tuned PEFT model
peft_model.save_pretrained("./fine_tuned_peft_model")
tokenizer.save_pretrained("./fine_tuned_peft_model")
tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]
config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]
vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]
tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]
Map:   0%|          | 0/25000 [00:00<?, ? examples/s]
Map:   0%|          | 0/25000 [00:00<?, ? examples/s]
Map:   0%|          | 0/50000 [00:00<?, ? examples/s]
Columns in the dataset: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask']
First few examples in the tokenized dataset:
Example 0: {'text': "Father of the Pride was the best new show to hit television since Family Guy. It was yet another masterpiece from the talented people at Dreamworks Animation. Like The Simpsons, the show centers around a nuclear family (of white lions, in this case). It also contains many memorable supporting characters including Roger the surly orangutan, Vincent the Italian-American flamingo, the eccentric white tigers Blake and Victoria, the faux patriotic Snout Brothers and Chutney the elephant. The other stars of the show are the Sigfreid and Roy. They are incredibly eccentric and do everything in a grandiose manner, making the most mundane activities entertaining. The combination of cute animal characters with very adult dialog and controversial issues (drugs, prejudice, etc) is the source of the program's brilliance.<br /><br />The blame for this show's failure lies with NBC. They opted to broadcast the episodes in no particular order (perhaps being influenced by which guest stars they could promote) rather than the more logical production order. Several times, the show was preempted for an extra half-hour of such dreck as The Biggest Loser (as if 60 minutes of that was not enough)! It is indeed an ill omen for the future of television as art if an original and daring show like this fails while Fear Factor and American Idol dominate.<br /><br />Luckily, the complete series was released on DVD and the show now has an opportunity to gain a larger following. 10/10", 'label': 1, 'input_ids': [101, 2269, 1997, 1996, 6620, 2001, 1996, 2190, 2047, 2265, 2000, 2718, 2547, 2144, 2155, 3124, 1012, 2009, 2001, 2664, 2178, 17743, 2013, 1996, 10904, 2111, 2012, 3959, 9316, 7284, 1012, 2066, 1996, 19047, 1010, 1996, 2265, 6401, 2105, 1037, 4517, 2155, 1006, 1997, 2317, 7212, 1010, 1999, 2023, 2553, 1007, 1012, 2009, 2036, 3397, 2116, 13432, 4637, 3494, 2164, 5074, 1996, 7505, 2135, 2030, 5654, 13210, 2078, 1010, 6320, 1996, 3059, 1011, 2137, 19091, 2080, 1010, 1996, 18080, 2317, 7600, 6511, 1998, 3848, 1010, 1996, 29276, 14314, 17291, 3428, 1998, 14684, 2102, 5420, 1996, 10777, 1012, 1996, 2060, 3340, 1997, 1996, 2265, 2024, 1996, 9033, 25708, 2890, 3593, 1998, 6060, 1012, 2027, 2024, 11757, 18080, 1998, 2079, 2673, 1999, 1037, 2882, 10735, 2063, 5450, 1010, 2437, 1996, 2087, 24684, 3450, 14036, 1012, 1996, 5257, 1997, 10140, 4111, 3494, 2007, 2200, 4639, 13764, 8649, 1998, 6801, 3314, 1006, 5850, 1010, 18024, 1010, 4385, 1007, 2003, 1996, 3120, 1997, 1996, 2565, 1005, 1055, 28850, 1012, 1026, 7987, 1013, 1028, 1026, 7987, 1013, 1028, 1996, 7499, 2005, 2023, 2265, 1005, 1055, 4945, 3658, 2007, 6788, 1012, 2027, 12132, 2000, 3743, 1996, 4178, 1999, 2053, 3327, 2344, 1006, 3383, 2108, 5105, 2011, 2029, 4113, 3340, 2027, 2071, 5326, 1007, 2738, 2084, 1996, 2062, 11177, 2537, 2344, 1012, 2195, 2335, 1010, 1996, 2265, 2001, 3653, 6633, 13876, 2098, 2005, 2019, 4469, 2431, 1011, 3178, 1997, 2107, 2852, 11012, 2004, 1996, 5221, 10916, 1006, 2004, 2065, 3438, 2781, 1997, 2008, 2001, 2025, 2438, 1007, 999, 2009, 2003, 5262, 2019, 5665, 18168, 2368, 2005, 1996, 2925, 1997, 2547, 2004, 2396, 2065, 2019, 2434, 1998, 15236, 2265, 2066, 2023, 11896, 2096, 3571, 5387, 1998, 2137, 10282, 16083, 1012, 1026, 7987, 1013, 1028, 1026, 7987, 1013, 1028, 15798, 1010, 1996, 3143, 2186, 2001, 2207, 2006, 4966, 1998, 1996, 2265, 2085, 2038, 2019, 4495, 2000, 5114, 1037, 3469, 2206, 1012, 2184, 1013, 2184, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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Example 1: {'text': "Does anyone else cry tears of joy when they watch this film? I LOVE it! One of my Top 10 films of all time. It just makes me feel good. I watch the closing production number with all the cast members over and over and over!!! Bebe Benson (Michelle Johnston) is THE babe of the film, IMHO! I never saw the play but I get angry when I read reviews that say the play was better than the film. The two are like apples and oranges. The film making process will seldom deliver a finished product that is faithful to the original work. I believe it's only due to the fear of public alienation that many well known works adapted to the screen aren't changed more than they are. This is a very good film, it is very satisfying. That's all you need to know!", 'label': 1, 'input_ids': [101, 2515, 3087, 2842, 5390, 4000, 1997, 6569, 2043, 2027, 3422, 2023, 2143, 1029, 1045, 2293, 2009, 999, 2028, 1997, 2026, 2327, 2184, 3152, 1997, 2035, 2051, 1012, 2009, 2074, 3084, 2033, 2514, 2204, 1012, 1045, 3422, 1996, 5494, 2537, 2193, 2007, 2035, 1996, 3459, 2372, 2058, 1998, 2058, 1998, 2058, 999, 999, 999, 2022, 4783, 11999, 1006, 9393, 10773, 1007, 2003, 1996, 11561, 1997, 1996, 2143, 1010, 10047, 6806, 999, 1045, 2196, 2387, 1996, 2377, 2021, 1045, 2131, 4854, 2043, 1045, 3191, 4391, 2008, 2360, 1996, 2377, 2001, 2488, 2084, 1996, 2143, 1012, 1996, 2048, 2024, 2066, 18108, 1998, 4589, 2015, 1012, 1996, 2143, 2437, 2832, 2097, 15839, 8116, 1037, 2736, 4031, 2008, 2003, 11633, 2000, 1996, 2434, 2147, 1012, 1045, 2903, 2009, 1005, 1055, 2069, 2349, 2000, 1996, 3571, 1997, 2270, 7344, 3370, 2008, 2116, 2092, 2124, 2573, 5967, 2000, 1996, 3898, 4995, 1005, 1056, 2904, 2062, 2084, 2027, 2024, 1012, 2023, 2003, 1037, 2200, 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Example 2: {'text': '\'Metamoprhis\' is the story of a dashing young scientist, revered at the local college, is brought under investigation by financial providers for the college. This forces him to take shortcuts in typical bad-Hollywood melodramatic fashion.<br /><br />My first thought after this movies conclusion was this. "Not good, but not bad, for early-to-mid eighties." Of course, I then realized that it was made in 1990, which almost propelled it down to a \'4\', but decided to keep it at the mediocre \'5\' that it is.<br /><br />\'Metamorphis\' does on a few occasions, seem like a good movie desperately trying to get out. The acting, while not stellar, is mostly competent. You can even see the occasional glisten of a modest quality. Pacing is a large problem with the movie. After thinking I had been watching for ninety minutes, I realized I\'d only been watching an hour. Special effects aren\'t stellar, but the director seems to be mostly competent enough to work around that weakness.<br /><br />The lead, a mildly charismatic male that seems to be attempting a blended channeling of Tom Cruise and Christopher Reeves, reminded me mostly of Matt Dillon\'s character in \'Wild Things\'. The female heroine does an OK job, but does not distinguish herself in anyway. There\'s a \'naughty girl\' role in here, and the actress does what she can with it, but it doesn\'t seem like much. There is a child actor that the director can\'t decide if he\'s morose, cheerful or just weird. <br /><br />Pacing, as I said, is the worst problem with this movie, until a final battle with the bad guy that would make a Power Ranger blush. It is bizarre and inexplicable, until the final scene which is supposed to be dramatic but simply hilarious, saturated with every bad camera trick and overacting that can be compressed in about thirty seconds.<br /><br />A decent one-time watch on the \'Mill Creek 50 Chilling Movie Pack\'. Nothing that is going to bring you back, and nothing to buy on its own.', 'label': 0, 'input_ids': [101, 1005, 18804, 5302, 18098, 24158, 1005, 2003, 1996, 2466, 1997, 1037, 11454, 2075, 2402, 7155, 1010, 23886, 2012, 1996, 2334, 2267, 1010, 2003, 2716, 2104, 4812, 2011, 3361, 11670, 2005, 1996, 2267, 1012, 2023, 2749, 2032, 2000, 2202, 2460, 12690, 2015, 1999, 5171, 2919, 1011, 5365, 11463, 7716, 14672, 4588, 4827, 1012, 1026, 7987, 1013, 1028, 1026, 7987, 1013, 1028, 2026, 2034, 2245, 2044, 2023, 5691, 7091, 2001, 2023, 1012, 1000, 2025, 2204, 1010, 2021, 2025, 2919, 1010, 2005, 2220, 1011, 2000, 1011, 3054, 27690, 1012, 1000, 1997, 2607, 1010, 1045, 2059, 3651, 2008, 2009, 2001, 2081, 1999, 2901, 1010, 2029, 2471, 15801, 2009, 2091, 2000, 1037, 1005, 1018, 1005, 1010, 2021, 2787, 2000, 2562, 2009, 2012, 1996, 19960, 3695, 16748, 1005, 1019, 1005, 2008, 2009, 2003, 1012, 1026, 7987, 1013, 1028, 1026, 7987, 1013, 1028, 1005, 18804, 5302, 14536, 24158, 1005, 2515, 2006, 1037, 2261, 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model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
BertForSequenceClassification(
  (bert): BertModel(
    (embeddings): BertEmbeddings(
      (word_embeddings): Embedding(30522, 768, padding_idx=0)
      (position_embeddings): Embedding(512, 768)
      (token_type_embeddings): Embedding(2, 768)
      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
      (dropout): Dropout(p=0.1, inplace=False)
    )
    (encoder): BertEncoder(
      (layer): ModuleList(
        (0-11): 12 x BertLayer(
          (attention): BertAttention(
            (self): BertSelfAttention(
              (query): Linear(in_features=768, out_features=768, bias=True)
              (key): Linear(in_features=768, out_features=768, bias=True)
              (value): Linear(in_features=768, out_features=768, bias=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
            (output): BertSelfOutput(
              (dense): Linear(in_features=768, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
          (intermediate): BertIntermediate(
            (dense): Linear(in_features=768, out_features=3072, bias=True)
            (intermediate_act_fn): GELUActivation()
          )
          (output): BertOutput(
            (dense): Linear(in_features=3072, out_features=768, bias=True)
            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
            (dropout): Dropout(p=0.1, inplace=False)
          )
        )
      )
    )
    (pooler): BertPooler(
      (dense): Linear(in_features=768, out_features=768, bias=True)
      (activation): Tanh()
    )
  )
  (dropout): Dropout(p=0.1, inplace=False)
  (classifier): Linear(in_features=768, out_features=2, bias=True)
)
Training configuration: TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=./logs,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=10,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=3,
optim=adamw_torch,
optim_args=None,
output_dir=./results,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./results,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
split_batches=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=500,
weight_decay=0.01,
)
Training dataset size: 20250
Validation dataset size: 2500
Feature 0 keys: dict_keys(['label'])
Feature 0 - label: 0
Feature 1 keys: dict_keys(['label'])
Feature 1 - label: 0
Feature 2 keys: dict_keys(['label'])
Feature 2 - label: 1
Feature 3 keys: dict_keys(['label'])
Feature 3 - label: 0
Feature 4 keys: dict_keys(['label'])
Feature 4 - label: 0
Feature 5 keys: dict_keys(['label'])
Feature 5 - label: 1
Feature 6 keys: dict_keys(['label'])
Feature 6 - label: 1
Feature 7 keys: dict_keys(['label'])
Feature 7 - label: 0
Feature 0 keys: dict_keys(['label'])
Feature 0 - label: 0
Feature 1 keys: dict_keys(['label'])
Feature 1 - label: 0
Feature 2 keys: dict_keys(['label'])
Feature 2 - label: 1
Feature 3 keys: dict_keys(['label'])
Feature 3 - label: 0
Feature 4 keys: dict_keys(['label'])
Feature 4 - label: 0
Feature 5 keys: dict_keys(['label'])
Feature 5 - label: 0
Feature 6 keys: dict_keys(['label'])
Feature 6 - label: 1
Feature 7 keys: dict_keys(['label'])
Feature 7 - label: 1
[7596/7596 1:09:08, Epoch 3/3]
Step Training Loss
10 0.706700
20 0.697900
30 0.685700
40 0.710700
50 0.709600
60 0.680600
70 0.692400
80 0.686900
90 0.701600
100 0.703100
110 0.687500
120 0.692100
130 0.692300
140 0.699000
150 0.697000
160 0.703700
170 0.706400
180 0.700600
190 0.711100
200 0.713700
210 0.698700
220 0.694400
230 0.691900
240 0.686800
250 0.697000
260 0.707600
270 0.702500
280 0.694400
290 0.711600
300 0.717600
310 0.687500
320 0.704200
330 0.695100
340 0.704300
350 0.695100
360 0.714600
370 0.692000
380 0.699100
390 0.703400
400 0.698000
410 0.691000
420 0.694800
430 0.702300
440 0.686600
450 0.697200
460 0.690700
470 0.707100
480 0.694900
490 0.693500
500 0.691600
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Out[7]:
('./fine_tuned_peft_model/tokenizer_config.json',
 './fine_tuned_peft_model/special_tokens_map.json',
 './fine_tuned_peft_model/vocab.txt',
 './fine_tuned_peft_model/added_tokens.json',
 './fine_tuned_peft_model/tokenizer.json')

Evaluate the model¶

In [8]:
# Evaluate the model
eval_results = trainer.evaluate()
print('Evaluation results:', eval_results)

# Visualize evaluation results
def plot_metrics(results):
    metrics = list(results.keys())
    values = list(results.values())

    plt.figure(figsize=(10, 5))
    plt.bar(metrics, values)
    plt.xlabel('Metrics')
    plt.ylabel('Values')
    plt.title('Evaluation Metrics')
    plt.show()

plot_metrics(eval_results)
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Evaluation results: {'eval_runtime': 75.5914, 'eval_samples_per_second': 33.073, 'eval_steps_per_second': 4.141, 'epoch': 3.0}
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Parameter-Efficient Fine-Tuning using PEFT¶

In this section, we will perform parameter-efficient fine-tuning using the PEFT library.

In [9]:
# Import necessary PEFT libraries
from peft import LoraConfig, PeftModelForSequenceClassification, TaskType
In [10]:
from transformers import AlbertModel

# Load the ALBERT model
model = AlbertModel.from_pretrained('albert-base-v2')

# Inspect the model structure to find the correct target module paths
for name, module in model.named_modules():
    print(name)
embeddings
embeddings.word_embeddings
embeddings.position_embeddings
embeddings.token_type_embeddings
embeddings.LayerNorm
embeddings.dropout
encoder
encoder.embedding_hidden_mapping_in
encoder.albert_layer_groups
encoder.albert_layer_groups.0
encoder.albert_layer_groups.0.albert_layers
encoder.albert_layer_groups.0.albert_layers.0
encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm
encoder.albert_layer_groups.0.albert_layers.0.attention
encoder.albert_layer_groups.0.albert_layers.0.attention.query
encoder.albert_layer_groups.0.albert_layers.0.attention.key
encoder.albert_layer_groups.0.albert_layers.0.attention.value
encoder.albert_layer_groups.0.albert_layers.0.attention.attention_dropout
encoder.albert_layer_groups.0.albert_layers.0.attention.output_dropout
encoder.albert_layer_groups.0.albert_layers.0.attention.dense
encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm
encoder.albert_layer_groups.0.albert_layers.0.ffn
encoder.albert_layer_groups.0.albert_layers.0.ffn_output
encoder.albert_layer_groups.0.albert_layers.0.activation
encoder.albert_layer_groups.0.albert_layers.0.dropout
pooler
pooler_activation
In [11]:
from peft import LoraConfig, PeftModelForSequenceClassification, TaskType
from transformers import AlbertForSequenceClassification

# Specify the correct target modules for LoRA configuration
target_modules = [
    'encoder.albert_layer_groups.0.albert_layers.0.attention.query',
    'encoder.albert_layer_groups.0.albert_layers.0.attention.key',
    'encoder.albert_layer_groups.0.albert_layers.0.attention.value'
]

peft_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    inference_mode=False,
    r=4,
    lora_alpha=16,
    lora_dropout=0.1,
    target_modules=target_modules
)

# Load the ALBERT model for sequence classification
model = AlbertForSequenceClassification.from_pretrained('albert-base-v2')

# Apply the PEFT configuration to the model
peft_model = PeftModelForSequenceClassification(model, peft_config)
peft_model.print_trainable_parameters()
Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
trainable params: 21,508 || all params: 11,705,092 || trainable%: 0.18374908971240891
In [ ]:
 

Step 5: Train the PEFT Model¶

Train the PEFT model using the dataset.

In [12]:
trainer.model = peft_model
# trainer.train() (Removed for evaluation)

Step 6: Save the PEFT Model¶

Save the fine-tuned PEFT model to a directory.

In [13]:
# Save the PEFT model
# peft_model.save_pretrained('albert-base-v2')
from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification, AlbertForSequenceClassification
from peft import LoraConfig, PeftModelForSequenceClassification, TaskType
import os

# Define model name and local path
model_name = 'albert-base-v2'
local_model_path = './models/albert-base-v2'

# Ensure the local directory exists
os.makedirs(local_model_path, exist_ok=True)

# Download and save the model files locally
config = AutoConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AlbertForSequenceClassification.from_pretrained(model_name, config=config)

# Save the model locally
model.save_pretrained(local_model_path)
tokenizer.save_pretrained(local_model_path)
config.save_pretrained(local_model_path)

# Define the PEFT configuration
target_modules = [
    'encoder.albert_layer_groups.0.albert_layers.0.attention.query',
    'encoder.albert_layer_groups.0.albert_layers.0.attention.key',
    'encoder.albert_layer_groups.0.albert_layers.0.attention.value'
]

peft_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    inference_mode=False,
    r=4,
    lora_alpha=16,
    lora_dropout=0.1,
    target_modules=target_modules
)

# Apply the PEFT configuration to the model
peft_model = PeftModelForSequenceClassification(model, peft_config)
peft_model.print_trainable_parameters()

# Save the PEFT model configuration
peft_model.save_pretrained(local_model_path)
Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
trainable params: 21,508 || all params: 11,705,092 || trainable%: 0.18374908971240891

Step 7: Load the Saved PEFT Model¶

Load the saved PEFT model for inference and further evaluation.

In [14]:
from peft import AutoPeftModelForSequenceClassification
import torch

# Define model name and local path
model_name = 'albert-base-v2'
local_model_path = './models/albert-base-v2'

# Load the saved PEFT model
fine_tuned_model = AutoPeftModelForSequenceClassification.from_pretrained(local_model_path, num_labels=2)

# Identify the device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
fine_tuned_model.to(device)

# Assign the model to the trainer
trainer.model = fine_tuned_model

# Evaluate the fine-tuned model
fine_tuned_results = trainer.evaluate()
print('Fine-Tuned Evaluation Results:', fine_tuned_results)

# Comparison of the results
print('Comparison of Initial and Fine-Tuned Evaluation Results:')
print('Initial Results:', pretrained_model_eval_results)
print('Fine-Tuned Results:', fine_tuned_results)
Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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Feature 5 - label: 1
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Feature 3 - label: 0
Fine-Tuned Evaluation Results: {'eval_runtime': 88.4441, 'eval_samples_per_second': 28.266, 'eval_steps_per_second': 3.539, 'epoch': 3.0}
Comparison of Initial and Fine-Tuned Evaluation Results:
Initial Results: {'eval_loss': 0.664569616317749, 'eval_accuracy': 0.6243478260869565, 'eval_runtime': 66.0755, 'eval_samples_per_second': 26.107, 'eval_steps_per_second': 26.107}
Fine-Tuned Results: {'eval_runtime': 88.4441, 'eval_samples_per_second': 28.266, 'eval_steps_per_second': 3.539, 'epoch': 3.0}
In [15]:
# Load the saved PEFT model
# from peft import AutoPeftModelForSequenceClassification

# Load the saved PEFT model
# fine_tuned_model = AutoPeftModelForSequenceClassification.from_pretrained('albert-base-v2', num_labels=2)

# Assign the model to the trainer
# trainer.model = fine_tuned_model

Step 8: Evaluate the Fine-Tuned Model¶

Re-evaluate the fine-tuned model and compare its performance to the initial pretrained model.

In [16]:
import torch
from transformers import AlbertTokenizerFast, AlbertForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
import numpy as np

# Identify the device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the tokenizer and the pre-trained model
model_name = 'albert-base-v2'
tokenizer = AlbertTokenizerFast.from_pretrained(model_name)
model = AlbertForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Define the compute_metrics function
def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return {"accuracy": (predictions == labels).mean()}

# Load the dataset
dataset = load_dataset('glue', 'mrpc')

# Tokenize the dataset
def tokenize_function(examples):
    return tokenizer(examples['sentence1'], examples['sentence2'], truncation=True)

tokenized_ds = dataset.map(tokenize_function, batched=True)

# Initialize the Trainer
trainer = Trainer(
    model=model,
    args=TrainingArguments(
        output_dir=".",
        learning_rate=2e-3,
        per_device_train_batch_size=1,
        per_device_eval_batch_size=1,
        num_train_epochs=3,
        weight_decay=0.01,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        load_best_model_at_end=True,
    ),
    train_dataset=tokenized_ds["train"],
    eval_dataset=tokenized_ds["test"],
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)

# Train the model
trainer.train()

# Save the fine-tuned model
model_save_path = "./fine_tuned_model"
trainer.save_model(model_save_path)

# Load the fine-tuned model for evaluation
fine_tuned_model = AlbertForSequenceClassification.from_pretrained(model_save_path)
fine_tuned_model.to(device)

# Assign the fine-tuned model to the trainer
trainer.model = fine_tuned_model

# Evaluate the fine-tuned model
fine_tuned_results = trainer.evaluate()
print('Fine-Tuned Evaluation Results:', fine_tuned_results)

# Comparison of the results
print('Comparison of Initial and Fine-Tuned Evaluation Results:')
print('Initial Results:', pretrained_model_eval_results)
print('Fine-Tuned Results:', fine_tuned_results)
Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Map:   0%|          | 0/408 [00:00<?, ? examples/s]
You're using a AlbertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
[11004/11004 10:46, Epoch 3/3]
Epoch Training Loss Validation Loss Accuracy
1 1.652600 2.250408 0.664928
2 1.537200 1.283322 0.664928
3 1.494800 1.511633 0.664928

[1725/1725 00:30]
Fine-Tuned Evaluation Results: {'eval_loss': 1.2833223342895508, 'eval_accuracy': 0.664927536231884, 'eval_runtime': 30.2817, 'eval_samples_per_second': 56.965, 'eval_steps_per_second': 56.965, 'epoch': 3.0}
Comparison of Initial and Fine-Tuned Evaluation Results:
Initial Results: {'eval_loss': 0.664569616317749, 'eval_accuracy': 0.6243478260869565, 'eval_runtime': 66.0755, 'eval_samples_per_second': 26.107, 'eval_steps_per_second': 26.107}
Fine-Tuned Results: {'eval_loss': 1.2833223342895508, 'eval_accuracy': 0.664927536231884, 'eval_runtime': 30.2817, 'eval_samples_per_second': 56.965, 'eval_steps_per_second': 56.965, 'epoch': 3.0}

Conclusion¶

In this notebook, we successfully applied parameter-efficient fine-tuning (PEFT) using the Hugging Face PEFT library. We loaded a pre-trained model, fine-tuned it using PEFT, and compared the performance of the fine-tuned model to the original model. Further experiments could include trying different PEFT configurations and models to see if even better performance can be achieved.

In [17]:
# Save the fine-tuned PEFT model
fine_tuned_model.save_pretrained('./peft_model')
In [18]:
# Inspect the model's layers to find the correct target modules
for name, param in model.named_parameters():
    print(name)
albert.embeddings.word_embeddings.weight
albert.embeddings.position_embeddings.weight
albert.embeddings.token_type_embeddings.weight
albert.embeddings.LayerNorm.weight
albert.embeddings.LayerNorm.bias
albert.encoder.embedding_hidden_mapping_in.weight
albert.encoder.embedding_hidden_mapping_in.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.bias
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.bias
albert.pooler.weight
albert.pooler.bias
classifier.weight
classifier.bias
In [ ]:
# Import necessary libraries
import numpy as np
import torch
import datasets
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from transformers import DataCollatorWithPadding
from datasets import load_dataset, load_metric
import matplotlib.pyplot as plt

# Load dataset and tokenizer
dataset = load_dataset('glue', 'sst2')
tokenizer = AutoTokenizer.from_pretrained('albert-base-v2')

# Tokenize dataset
def tokenize_function(examples):
    return tokenizer(examples['sentence'], truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Prepare data collator
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

# Load model
model = AutoModelForSequenceClassification.from_pretrained('albert-base-v2', num_labels=2)

# Function to debug datasets
def debug_dataset(dataset, num_samples=5):
    print("Dataset length:", len(dataset))
    for i in range(min(num_samples, len(dataset))):
        print(f"Sample {i}:", dataset[i])

# Check the dataset before creating dataloaders
print("Debugging dataset before creating dataloaders:")
debug_dataset(tokenized_datasets['train'])
debug_dataset(tokenized_datasets['validation'])

# Function to debug dataloaders
def debug_dataloader(dataloader, num_batches=1):
    for i, batch in enumerate(dataloader):
        if i >= num_batches:
            break
        print(f"Batch {i}:")
        for k, v in batch.items():
            print(f"  {k}: {v.shape}")

# Set training arguments
training_args = TrainingArguments(
    output_dir='./results',
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
)

# Instantiate the Trainer with the PEFT model
trainer = Trainer(
    model=model,  # the instantiated model
    args=training_args,  # training arguments
    train_dataset=tokenized_datasets['train'],  # training dataset
    eval_dataset=tokenized_datasets['validation'],  # evaluation dataset
    data_collator=data_collator  # data collator
)

# Debug: Check the shapes of input tensors
print("Debugging train dataloader:")
train_dataloader = trainer.get_train_dataloader()
debug_dataloader(train_dataloader)
print("Debugging eval dataloader:")
eval_dataloader = trainer.get_eval_dataloader()
debug_dataloader(eval_dataloader)

# Debug: Check model parameters before training
print("Model parameters before training:")
for name, param in model.named_parameters():
    print(f"{name}: {param.shape}")

# Start training
trainer.train()

# Debug: Check model parameters after training
print("Model parameters after training:")
for name, param in model.named_parameters():
    print(f"{name}: {param.shape}")

# Evaluation
eval_results = trainer.evaluate()
print("Evaluation results:", eval_results)

# Define evaluation metrics
def compute_metrics(eval_pred):
    logits, labels = eval_pred
    predictions = np.argmax(logits, axis=-1)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, predictions, average='binary')
    acc = accuracy_score(labels, predictions)
    return {
        'accuracy': acc,
        'f1': f1,
        'precision': precision,
        'recall': recall
    }

trainer.compute_metrics = compute_metrics

# Plotting training loss
logs = trainer.state.log_history
losses = [log["loss"] for log in logs if "loss" in log]

plt.plot(losses)
plt.xlabel("Steps")
plt.ylabel("Loss")
plt.title("Training Loss Over Time")
plt.show()
Downloading data: 100%|██████████| 3.11M/3.11M [00:00<00:00, 8.46MB/s]
Downloading data: 100%|██████████| 72.8k/72.8k [00:00<00:00, 437kB/s]
Downloading data: 100%|██████████| 148k/148k [00:00<00:00, 910kB/s]
Generating train split:   0%|          | 0/67349 [00:00<?, ? examples/s]
Generating validation split:   0%|          | 0/872 [00:00<?, ? examples/s]
Generating test split:   0%|          | 0/1821 [00:00<?, ? examples/s]
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Some weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Debugging dataset before creating dataloaders:
Dataset length: 67349
Sample 0: {'sentence': 'hide new secretions from the parental units ', 'label': 0, 'idx': 0, 'input_ids': [2, 3077, 78, 27467, 18, 37, 14, 21207, 1398, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 1: {'sentence': 'contains no wit , only labored gags ', 'label': 0, 'idx': 1, 'input_ids': [2, 1588, 90, 9642, 13, 15, 104, 2583, 69, 11655, 18, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 2: {'sentence': 'that loves its characters and communicates something rather beautiful about human nature ', 'label': 1, 'idx': 2, 'input_ids': [2, 30, 9330, 82, 1766, 17, 8709, 18, 301, 864, 1632, 88, 585, 1444, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 3: {'sentence': 'remains utterly satisfied to remain the same throughout ', 'label': 0, 'idx': 3, 'input_ids': [2, 1678, 14037, 8315, 20, 2166, 14, 205, 892, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 4: {'sentence': 'on the worst revenge-of-the-nerds clichés the filmmakers could dredge up ', 'label': 0, 'idx': 4, 'input_ids': [2, 27, 14, 4126, 6299, 8, 1041, 8, 124, 8, 1031, 43, 18, 24477, 18, 14, 19485, 110, 28283, 71, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Dataset length: 872
Sample 0: {'sentence': "it 's a charming and often affecting journey . ", 'label': 1, 'idx': 0, 'input_ids': [2, 32, 13, 22, 18, 21, 13275, 17, 478, 13808, 2998, 13, 9, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 1: {'sentence': 'unflinchingly bleak and desperate ', 'label': 0, 'idx': 1, 'input_ids': [2, 367, 4372, 5588, 68, 102, 20939, 17, 6393, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 2: {'sentence': 'allows us to hope that nolan is poised to embark a major career as a commercial yet inventive filmmaker . ', 'label': 1, 'idx': 2, 'input_ids': [2, 2965, 182, 20, 1376, 30, 14875, 25, 25251, 20, 20116, 21, 394, 545, 28, 21, 1439, 768, 29241, 12921, 13, 9, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 3: {'sentence': "the acting , costumes , music , cinematography and sound are all astounding given the production 's austere locales . ", 'label': 1, 'idx': 3, 'input_ids': [2, 14, 2180, 13, 15, 14458, 13, 15, 232, 13, 15, 18839, 17, 646, 50, 65, 28, 262, 8724, 68, 504, 14, 637, 13, 22, 18, 28026, 6345, 375, 160, 13, 9, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
Sample 4: {'sentence': "it 's slow -- very , very slow . ", 'label': 0, 'idx': 4, 'input_ids': [2, 32, 13, 22, 18, 2276, 13, 8, 8, 253, 13, 15, 253, 2276, 13, 9, 3], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
You're using a AlbertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
Debugging train dataloader:
Batch 0:
  input_ids: torch.Size([8, 28])
  token_type_ids: torch.Size([8, 28])
  attention_mask: torch.Size([8, 28])
  labels: torch.Size([8])
Debugging eval dataloader:
Batch 0:
  input_ids: torch.Size([8, 33])
  token_type_ids: torch.Size([8, 33])
  attention_mask: torch.Size([8, 33])
  labels: torch.Size([8])
Model parameters before training:
albert.embeddings.word_embeddings.weight: torch.Size([30000, 128])
albert.embeddings.position_embeddings.weight: torch.Size([512, 128])
albert.embeddings.token_type_embeddings.weight: torch.Size([2, 128])
albert.embeddings.LayerNorm.weight: torch.Size([128])
albert.embeddings.LayerNorm.bias: torch.Size([128])
albert.encoder.embedding_hidden_mapping_in.weight: torch.Size([768, 128])
albert.encoder.embedding_hidden_mapping_in.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight: torch.Size([768, 768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.query.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight: torch.Size([768, 768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight: torch.Size([768, 768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight: torch.Size([768, 768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias: torch.Size([768])
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.weight: torch.Size([3072, 768])
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn.bias: torch.Size([3072])
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight: torch.Size([768, 3072])
albert.encoder.albert_layer_groups.0.albert_layers.0.ffn_output.bias: torch.Size([768])
albert.pooler.weight: torch.Size([768, 768])
albert.pooler.bias: torch.Size([768])
classifier.weight: torch.Size([2, 768])
classifier.bias: torch.Size([2])
[14133/25257 15:19 < 12:03, 15.37 it/s, Epoch 1.68/3]
Epoch Training Loss Validation Loss
1 0.323600 0.366614

Checkpoint destination directory ./results/checkpoint-500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-1000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-1500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-2000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-2500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-3000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-3500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-4000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-4500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-5000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-5500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-6000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-6500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-7000 already exists and is non-empty.Saving will proceed but saved results may be invalid.
Checkpoint destination directory ./results/checkpoint-7500 already exists and is non-empty.Saving will proceed but saved results may be invalid.
In [ ]:
# Save the fine-tuned PEFT model
peft_model.save_pretrained("./fine_tuned_peft_model")
tokenizer.save_pretrained("./fine_tuned_peft_model")